Flexible interactive data visualization enabled by dynamic attributes

ABSTRACT

A method and system are provided for interactive data visualization. The method includes analyzing a data source used with an initial query to identify a set of default categories available for user selection. The method further includes dynamically determining pre-set values for categorical portions and numerical portions of vocabularies for user selection using data analytics on the data source. The method also includes providing the user with a capability to specify other values for the portions. The method additionally includes dynamically generating multiple sub-queries to the data source for the values for the portions, wherein at least one of the multiple sub-queries is dynamically generated for at least one of the other values specified by the user. The method further includes combining result sets for the multiple sub-queries. The method also includes generating a data visualization of the combined result sets and displaying the data visualization.

BACKGROUND Technical Field

The present invention relates generally to information processing and,in particular, to flexible interactive data visualization enabled bydynamic attributes.

Description of the Related Art

Today's data visualization enables end users to (a) explore their data;(b) view the corresponding returned result set in graphical forms suchas pie charts and histograms. End users typically explore their data byinteracting (using interactions such as drill down, trace up andcombine) with the visualized graph of the returned result set. However,programmers of the data visualization have total control of what and howend users could interact with their data. If programmers did notanticipate or include certain data attributes or views, end users wouldnot be able to interact with the visualized graph the way they need. Endusers must ask the programmers to re-program the data visualization tomeet their needs, and would normally wait many months for the revisedcapability. End users are thus reliant on data visualization programmersin the aforementioned scenarios.

The common user interactions with visualized graph are: (i) drill down,which involves narrowing down from a current visualization view; (ii)trace up, which expands the data scope from an initial datavisualization from an original data source; and (iii) combine, whereexisting categories are grouped into larger groups with less categories.However, the latest technologies available today do not support all ofthese common user interactions. The existing solutions have some severerestrictions. For example, one severe restriction is that current userinteractions with data visualization are rigid, fixed, preset, andcontrolled entirely by programmers. Another severe restriction is thatno current solution exists that supports dynamic queries out of the datacontext of original data visualization previously shown.

Thus, there is a need for flexible interactive data visualizationenabled by dynamic attributes.

SUMMARY

According to an aspect of the present principles, a method is providedfor interactive data visualization. The method includes analyzing, by ahardware processor, a data source used with an initial query submittedby a user to identify a set of default categories available for userselection. The method further includes dynamically determining, by thehardware processor responsive to a user request directed to at least oneof the default categories, pre-set values for categorical portions andnumerical portions of vocabularies for user selection using dataanalytics on the data source. The method also includes providing, by auser interface, the user with a capability to specify other values forthe categorical portions and the numerical portions of the vocabularies.The method additionally includes dynamically generating, by the hardwareprocessor, multiple sub-queries to the data source for the values forthe categorical portions and numerical portions of the vocabularies,wherein at least one of the multiple sub-queries is dynamicallygenerated for at least one of the other values specified by the user.The method further includes combining, by the hardware processor, resultsets for the multiple sub-queries. The method also includes generating,by the hardware processor, a data visualization of the combined resultsets and displaying the data visualization on a display device.

According to another aspect of the present principles, a computerprogram product is provided for interactive data visualization. Thecomputer program product includes a non-transitory computer readablestorage medium having program instructions embodied therewith. Theprogram instructions are executable by a computer to cause the computerto perform a method. The method includes analyzing, by a hardwareprocessor, a data source used with an initial query submitted by a userto identify a set of default categories available for user selection.The method further includes dynamically determining, by the hardwareprocessor responsive to a user request directed to at least one of thedefault categories, pre-set values for categorical portions andnumerical portions of vocabularies for user selection using dataanalytics on the data source. The method also includes providing, by auser interface, the user with a capability to specify other values forthe categorical portions and the numerical portions of the vocabularies.The method additionally includes dynamically generating, by the hardwareprocessor, multiple sub-queries to the data source for the values forthe categorical portions and numerical portions of the vocabularies,wherein at least one of the multiple sub-queries is dynamicallygenerated for at least one of the other values specified by the user.The method further includes combining, by the hardware processor, resultsets for the multiple sub-queries. The method also includes generating,by the hardware processor, a data visualization of the combined resultsets and displaying the data visualization on a display device.

According to yet another aspect of the present principles, a system isprovided for interactive data visualization. The system includes ahardware processor, configured to analyze a data source used with aninitial query submitted by a user to identify a set of defaultcategories available for user selection. The hardware processor isfurther configured to dynamically determine, responsive to a userrequest directed to at least one of the default categories, pre-setvalues for categorical portions and numerical portions of vocabulariesfor user selection using data analytics on the data source. The hardwareprocessor is also configured to dynamically generate multiplesub-queries to the data source for the values for the categoricalportions and numerical portions of the vocabularies. The hardwareprocessor is additionally configured to combine result sets for themultiple sub-queries. The hardware processor is further configured togenerate a data visualization of the combined result sets. The systemfurther includes a display device configured to display the datavisualization. The system also includes a user interface configured toprovide the user with a capability to specify other values for thecategorical portions and the numerical portions of the vocabularies. Atleast one of the multiple sub-queries is dynamically generated for atleast one of the other values specified by the user.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles;

FIGS. 2-3 shows an exemplary method 200 for flexible interactive datavisualization enabled by dynamic attributes, in accordance with anembodiment of the present principles;

FIG. 4 shows a screenshot 400 corresponding to step 210 of FIG. 2,according to an embodiment of the present principles;

FIG. 5 shows a screenshot 500 corresponding to step 220 of FIG. 2,according to an embodiment of the present principles;

FIG. 6 shows a screenshot 600 corresponding to step 220C of FIG. 2, inaccordance with an embodiment of the present principles;

FIG. 7 shows a screenshot 700 corresponding to step 230 of FIG. 2,according to an embodiment of the present principles;

FIG. 8 shows a screenshot 800 corresponding to steps 230B, 230C, and230D of FIG. 3, according to an embodiment of the present principles;

FIG. 9 shows a constrained natural language dictionary 900 for use bythe present principles, in accordance with an embodiment of the presentprinciples;

FIG. 10 shows an additional step 240 for the method 200 of FIG. 2, inaccordance with an embodiment of the present principles;

FIG. 11 shows a screenshot 1100 corresponding to step 240 of FIG. 10,according to an embodiment of the present principles;

FIGS. 12-13 show an additional step 250 for the method 200 of FIG. 2, inaccordance with an embodiment of the present principles;

FIG. 14 shows a screenshot 1400 corresponding to steps 250A and 250B ofFIG. 12, according to an embodiment of the present principles;

FIG. 15 shows a screenshot 1500 corresponding to steps 250C and 250D ofFIG. 12, according to an embodiment of the present principles;

FIG. 16 shows a screenshot 1600 corresponding to steps 250E and 250F ofFIGS. 12 and 13, respectively, according to an embodiment of the presentprinciples;

FIG. 17 shows a screenshot 1700 corresponding to steps 250G and 250H ofFIG. 13, according to an embodiment of the present principles;

FIG. 18 shows an exemplary cloud computing environment, in accordancewith an embodiment of the present principles; and

FIG. 19 shows an exemplary set of functional abstraction layers providedby the cloud computing environment shown in FIG. 18, in accordance withan embodiment of the present principles.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present principles are directed to flexible interactive datavisualization enabled by dynamic attributes.

In an embodiment, the present principles can enable end users with thefollowing capabilities, with zero programming requirements. One suchcapability is interactive control flexibility to define different valuesegments with different value ranges, resulting in different numbers ofcategories and potentially new categories that are not pre-set. Anothersuch capability is performing dynamic queries that are out of the datacontext of original data visualization. Out of data context includes:(i) data attributes or vocabularies not present in the current datavisualization; and (ii) new data sources.

FIG. 1 shows an exemplary processing system 100 to which the presentprinciples may be applied, in accordance with an embodiment of thepresent principles. The processing system 100 includes at least oneprocessor (CPU) 104 operatively coupled to other components via a systembus 102. A cache 106, a Read Only Memory (ROM) 108, a Random AccessMemory (RAM) 110, an input/output (I/O) adapter 120, a sound adapter130, a network adapter 140, a user interface adapter 150, and a displayadapter 160, are operatively coupled to the system bus 102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present principles. The user input devices 152, 154,and 156 can be the same type of user input device or different types ofuser input devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. Moreover, one or more elements of FIG.1 can be implemented in a cloud configuration including, for example, ina distributed configuration. Additionally, one or more elements in FIG.1 may be implemented by a variety of devices, which include but are notlimited to, Digital Signal Processing (DSP) circuits, programmableprocessors, Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), Complex Programmable Logic Devices(CPLDs), and so forth. These and other variations of the processingsystem 100 are readily contemplated by one of ordinary skill in the artgiven the teachings of the present principles provided herein.

Further, it is to be appreciated that processing system 100 may performat least part of the method described herein including, for example, atleast part of method 200 of FIGS. 2-3 (including the additional stepsshown in FIGS. 10 and 12-13).

FIGS. 2-3 shows an exemplary method 200 for flexible interactive datavisualization enabled by dynamic attributes, in accordance with anembodiment of the present principles.

FIG. 4 shows a screenshot 400 corresponding to step 210 of FIG. 2,according to an embodiment of the present principles. In the screenshot400, the visualization 410 generated per step 210A is depicted.

At step 210, receive and process an initial query submitted by a userregarding the number of people having high blood pressure.

In an embodiment, step 210 includes step 210A.

At step 210A, generate a visualization 410 responsive to the initialquery.

FIG. 5 shows a screenshot 500 corresponding to step 220 of FIG. 2,according to an embodiment of the present principles.

At step 220, receive and process one or more words submitted by the userand that are included in a pre-built vocabulary in order to participatein the next data visualization. The one or more words can be submittedby the user in a text box (FIG. 5, block 577) that is configured to listparticipating data attributes. The one or more words consist of the word“gender”.

In an embodiment, step 220 includes steps 220A-220C.

At step 220A, prompt the user, using a user interface (UI) (FIG. 5,block 515), regarding the categories of “gender” to be used to group thedata in the visualization. The default categories (FIG. 5, block 520)available for selection in the user interface are determined based onanalytics of the data sources. The user also has the flexibility to addnew categories (FIG. 5, block 530) to be displayed in the visualization.

At step 220B, receive a reply to the prompt, from the user.

FIG. 6 shows a screenshot 600 corresponding to step 220C of FIG. 2, inaccordance with an embodiment of the present principles. In thescreenshot 600, the visualization 610 generated per step 220C isdepicted. The visualization 610 corresponds to the reply received atstep 220B selecting both “Female” and “Male” for gender.

At step 220C, generate a visualization 610 responsive to the reply.

FIG. 7 shows a screenshot 700 corresponding to step 230 of FIG. 2,according to an embodiment of the present principles.

At step 230, receive and process one or more other words submitted bythe user and that are also included in the pre-built vocabulary in orderto participate in the next data visualization. The one or more otherwords can be submitted by the user in a text box/UI widget (FIG. 7,block 777) that is configured to list participating data attributes. Theone or more other words consist of the word “age”, corresponding to theuser's intent to know the age distribution from the current bloodpressure visualization.

In an embodiment, step 230 includes steps 230A-230D.

At step 230A, prompt the user, using a user interface (UI), regardingthe value range for age to be used to group the data in thevisualization. The default age value range for selection in the userinterface is determined based on analytics of the data sources. However,the user has the ability to specify a wider or narrower value range(FIG. 7, block 720). The user also has the flexibility to specify thevalue segments for the age distribution (FIG. 7, block 730) to bedisplayed in the visualization. For example, the value segments could be“<30, 30-39, 40-49, >70”.

FIG. 8 shows a screenshot 800 corresponding to steps 230B, 230C, and230D of FIG. 3, according to an embodiment of the present principles. Inthe screenshot 800, the visualization 810 generated per step 230C isdepicted.

At step 230B, create a multi-segmented widget (FIG. 8, block 820) basedon the ranges specified by the user.

At step 230C, receive one or more user selections (FIG. 8, block 840) onthe multi-segmented widget.

At step 230D, generate a visualization 810 responsive to the one or moreuser selections received per step 230C.

A description will now be given regarding how end users are enabled,without programming requirements, to select data ranges and segments, inaccordance with an embodiment of the present principles.

In this scenario, steps 220 and 230 of FIG. 2 are enabled by having aconstrained natural language (CNL) dictionary. FIG. 9 shows aconstrained natural language dictionary 900 for use by the presentprinciples, in accordance with an embodiment of the present principles.The constrained natural language dictionary 900 includes a list ofvocabularies that reference the following:

1. Pre-built data mapping that allows end users to access data subsetsusing just the natural language vocabularies without a DBA task.Therefore, it meets non-programming requirements on the end users' side.

2. System generated metadata 901 and 902 of the vocabularies, thataugment the existing CNL dictionary to:

(i) dynamically determine category values (for categorical vocabularies)and numerical values (for numerical vocabularies) of the vocabulariesthrough data analytics (Hence, the initial values for userinterface/widget 520 and user interface/widget 720 in step 220 and step230, respectively, can be displayed).

(ii) store the value segments specified by the users in userinterface/widget 530 and user interface/widget 730.

Regarding category values, the same can include categories as values,while for numerical values, the same can include number (minimum,maximum, average, and so forth) as values.

Since the value segments are determined by the users, the informaticsqueries to the data sources have to be dynamically generated for each ofthe value segments and the resulting data visualization will becombining the result sets from each of the sub-queries of the valuesegments.

FIG. 10 shows an additional step 240 for the method 200 of FIG. 2, inaccordance with an embodiment of the present principles. FIG. 11 shows ascreenshot 1100 corresponding to step 240 of FIG. 10, according to anembodiment of the present principles.

At step 240, suggest to the user other vocabularies of nearest semanticproximity, such as ethnicity and education level, for further queryingthe data, given that “gender” and “age” have already been selected asdata attributes to be shown in the data visualization. In an embodiment,the suggestions can be provided by a user interface/widget (FIG. 11,block 1120). In an embodiment, the user/interface/widget can appearresponsive to a user hovering over an icon (FIG. 11, block 1150). In anembodiment, the icon is embodied as a lightbulb.

A description will now be given regarding performing dynamic querieswith vocabularies that are out of the data context of the original(initial) data visualization, without programming requirements, toselect data ranges and segments, in accordance with an embodiment of thepresent principles.

By using an existing lexical database, such as WordNet, for a given setof vocabularies, vocabularies in the same CNL dictionary that are ofnearest semantic proximity can be identified and suggested to the users.

The suggestions can include, but are not limited to, the following: (i)vocabularies with close semantic distance; (ii) related concepts/words;and (iii) usage statistics.

Regarding the suggestions relating to (i), consider the followingexample. “Age” and “Gender” are selected vocabularies that belongs tothe concept “Person”. From the lexical database, “ethnicity” and“education level” are attributes associated with “Person” and also haveclose semantic distances with the selected vocabularies.

Regarding the suggestions relating to (ii), consider the followingexample. “Person” and “Location” are related, so attributes of“location” would also be suggested.

Regarding the suggestions relating to (iii), consider the followingexample. All end users who selected “Age” would 90% of time select“Education level”. Hence, “Education level” would be suggested.

FIGS. 12-13 show an additional step 250 for the method 200 of FIG. 2, inaccordance with an embodiment of the present principles. FIGS. 12-13 andstep 250 relate to the following scenario. Logically the user knowsthere is a correlation between blood pressure and blood sugar, but thiscorrelation is not shown in the suggestions provided by the presentprinciples in step 240. The user wants to go beyond the suggestions instep 204 to add a new source of data such as blood sugar. The user addsa blood sugar data source and this shows up in the list of suggestionsand the user then chooses this new data source. The user wants to pullin this data source into the existing data visualization andautomatically adjust the resulting graph.

At step 250, add another data source to the existing data visualizationand automatically adjust the resulting graph.

In an embodiment, step 250 includes steps 250A-250D.

FIG. 14 shows a screenshot 1400 corresponding to steps 250A and 250B ofFIG. 12, according to an embodiment of the present principles.

At step 250A, receive a user selection of a source option from a list ofavailable source options (FIG. 14, block 1420).

At step 250B, receive a user selection of a new data source for thesource option selected at step 250A (FIG. 14, block 1430). The userselection of the new data source will supplement the previously selecteddata sources (for blood pressure).

FIG. 15 shows a screenshot 1500 corresponding to steps 250C and 250D ofFIG. 12, according to an embodiment of the present principles.

At step 250C, automatically update vocabulary suggestions provided tothe user, responsive to the data sources being modified/updated. In anembodiment, the suggestions can be provided by a user interface/widget(FIG. 15, block 1520). In an embodiment, the user/interface/widget canappear responsive to a user hovering over an icon (FIG. 15, block 1550).

At step 250D, receive a user selection of a vocabulary suggestion (FIG.15, block 1560).

FIG. 16 shows a screenshot 1600 corresponding to steps 250E and 250F ofFIGS. 12 and 13, respectively, according to an embodiment of the presentprinciples.

At step 250E, load the vocabulary selection selected by the user (perstep 250D) in the vocabulary box (FIG. 16, block 1677) and show relevantwidgets (FIG. 16, block 1620).

At step 250F, receive a user selection of a multi-segment widget with afixed number of segments (FIG. 16, block 1630) or some other userselection (FIG. 16, block 1640).

FIG. 17 shows a screenshot 1700 corresponding to steps 250G and 250H ofFIG. 13, according to an embodiment of the present principles.

At step 250G, receive a user adjustment of the segment ranges (FIG. 17,block 1730).

At step 250H, generate a visualization 1710 responsive to the useradjustment of the segment ranges received per step 250G.

Hence, as a result, the combined data visualization for blood pressureand blood sugar level is displayed according to the specified criteria.Also, with the new data source, there might be changes in the valuerange and segments, and our system would perform validation and warnusers of possible updates required.

When adding new data source such as “blood sugar”, we would validate ifthe sample population for the “blood sugar” fully or partially matchthat of the “blood pressure” group. If the two sample populations do notmatch (e.g. no overlap at all), our system would give end users awarning (since the new data source would have no effect on the currentgraph).

A description will now be given regarding performing dynamic querieswith data sources that are out of the data context of original datavisualization, in accordance with an embodiment of the presentprinciples.

On the server, there is a data source registry that keeps track of allthe data sources. It includes the following information (but not limitedto): (i) data source name; (ii) location of the data sources (e.g., JavaDataBase Connectivity (JDBC) Uniform Resource Locator (URL) fordatabases); (iii) credentials to access the data sources; and (iv)subdomain (e.g., blood pressure for Canadian population, blood sugarlevel for West Coast Canadian population, and so forth).

It's the Information Technology (IT) manager's job to register the datasources into the data source registry, because they are the user groupsthat have that information. Consequently, when the user wants to addmore data sources to be shown in the data visualization for step 250,the user can select them from the data source list retrieved from thatdata source registry. Data sources that are not registered in the dataregistry would not be visible for users to select.

After selecting the data sources for use in the visualization, a queryis submitted to the new data sources to get the query results to berendered in the existing visualization.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 18, illustrative cloud computing environment 1850is depicted. As shown, cloud computing environment 1850 includes one ormore cloud computing nodes 1810 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1854A, desktop computer 1854B, laptopcomputer 1854C, and/or automobile computer system 1854N may communicate.Nodes 1810 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1850to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1854A-N shown in FIG. 18 are intended to be illustrative only and thatcomputing nodes 1810 and cloud computing environment 1850 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 19, a set of functional abstraction layersprovided by cloud computing environment 1850 (FIG. 18) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 19 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 1960 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1961;RISC (Reduced Instruction Set Computer) architecture based servers 1962;servers 1963; blade servers 1964; storage devices 1965; and networks andnetworking components 1966. In some embodiments, software componentsinclude network application server software 1967 and database software1968.

Virtualization layer 1970 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1971; virtual storage 1972; virtual networks 1973, including virtualprivate networks; virtual applications and operating systems 1974; andvirtual clients 1975.

In one example, management layer 1980 may provide the functionsdescribed below. Resource provisioning 1981 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1982provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1983 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1984provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1985 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1990 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1991; software development and lifecycle management 1992;virtual classroom education delivery 1993; data analytics processing1994; transaction processing 1995; and flexible interactive datavisualization enabled by dynamic attributes 1996.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A method for interactive data visualization,comprising: analyzing, by a hardware processor, a data source used withan initial query submitted by a user to identify a set of defaultcategories available for user selection; dynamically determining, by thehardware processor responsive to a user request directed to at least oneof the default categories, pre-set values for categorical portions andnumerical portions of vocabularies for user selection using dataanalytics on the data source; providing, by a user interface, the userwith a capability to specify other values for the categorical portionsand the numerical portions of the vocabularies; dynamically generating,by the hardware processor, multiple sub-queries to the data source forthe values for the categorical portions and numerical portions of thevocabularies, wherein at least one of the multiple sub-queries isdynamically generated for at least one of the other values specified bythe user; combining, by the hardware processor, result sets for themultiple sub-queries; and generating, by the hardware processor, a datavisualization of the combined result sets and displaying the datavisualization on a display device.
 2. The method of claim 1, wherein atleast one of the multiple sub-queries is out of the data context of theinitial query by involving data attributes or vocabulary wordsuninvolved in the initial query.
 3. The method of claim 1, wherein atleast one of the multiple sub-queries is out of the data context of theinitial query by involving a new data source.
 4. The method of claim 1,further comprising accessing, by the hardware processor, a constrainednatural language dictionary comprising the vocabularies, wherein thevocabularies are configured to reference pre-built data mappings, thepre-built data mappings being configured to enable user access to datasubsets of the data source using the vocabularies and computer generatedmetadata of the vocabularies to augment the constrained natural languagedictionary.
 5. The method of claim 4, wherein said accessing stepcomprises generating the constrained natural language dictionaryresponsive to the initial query.
 6. The method of claim 4, wherein saidaccessing step comprises configuring the constrained natural languagedictionary responsive to the initial query.
 7. The method of claim 4,wherein said accessing step is performed as part of said determiningstep.
 8. The method of claim 4, further comprising prompting the userwith additional categories of nearest semantic proximity for furtherquerying of the data source.
 9. The method of claim 8, wherein saidprompting step comprises identifying and suggesting to the user, othervocabularies in the constrained natural language dictionary within asemantic proximity threshold to the vocabularies in the list.
 10. Themethod of claim 9, wherein said identifying step is performed using apredetermined existing lexical database for a set of vocabularies thatinclude the vocabularies and the other vocabularies.
 11. The method ofclaim 9, wherein the semantic proximity threshold is based on at leastone selected from the group of a semantic distance, a related word, arelated concept, usage statistics.
 12. The method of claim 1, furthercomprising: performing a validation process and sending a notificationto the user when an update to the values specified by the user isrequired and when a new data source has no overlap with the data source;and adding the new data source to a data source registry and enablingaccess to the new data source, responsive to a positive result for thevalidation process.
 13. A computer program product for interactive datavisualization, the computer program product comprising a non-transitorycomputer readable storage medium having program instructions embodiedtherewith, the program instructions executable by a computer to causethe computer to perform a method comprising: analyzing, by a hardwareprocessor, a data source used with an initial query submitted by a userto identify a set of default categories available for user selection;dynamically determining, by the hardware processor responsive to a userrequest directed to at least one of the default categories, pre-setvalues for categorical portions and numerical portions of vocabulariesfor user selection using data analytics on the data source; providing,by a user interface, the user with a capability to specify other valuesfor the categorical portions and the numerical portions of thevocabularies; dynamically generating, by the hardware processor,multiple sub-queries to the data source for the values for thecategorical portions and numerical portions of the vocabularies, whereinat least one of the multiple sub-queries is dynamically generated for atleast one of the other values specified by the user; combining, by thehardware processor, result sets for the multiple sub-queries; andgenerating, by the hardware processor, a data visualization of thecombined result sets and displaying the data visualization on a displaydevice.
 14. The computer program product of claim 13, wherein at leastone of the multiple sub-queries is out of the data context of theinitial query by involving data attributes or vocabulary wordsuninvolved in the initial query.
 15. The computer program product ofclaim 13, wherein at least one of the multiple sub-queries is out of thedata context of the initial query by involving a new data source. 16.The computer program product of claim 13, further comprising accessing,by the hardware processor, a constrained natural language dictionarycomprising the vocabularies, wherein the vocabularies are configured toreference pre-built data mappings, the pre-built data mappings beingconfigured to enable user access to data subsets of the data sourceusing the vocabularies and computer generated metadata of thevocabularies to augment the constrained natural language dictionary. 17.The computer program product of claim 16, further comprising promptingthe user with additional categories of nearest semantic proximity forfurther querying of the data source.
 18. The computer program product ofclaim 17, wherein said prompting step comprises identifying andsuggesting to the user, other vocabularies in the constrained naturallanguage dictionary within a semantic proximity threshold to thevocabularies in the list.
 19. The computer program product of claim 18,wherein said identifying step is performed using a predeterminedexisting lexical database for a set of vocabularies that include thevocabularies and the other vocabularies.
 20. A system for interactivedata visualization, comprising: a hardware processor, configured to:analyze a data source used with an initial query submitted by a user toidentify a set of default categories available for user selection;dynamically determine, responsive to a user request directed to at leastone of the default categories, pre-set values for categorical portionsand numerical portions of vocabularies for user selection using dataanalytics on the data source; dynamically generate multiple sub-queriesto the data source for the values for the categorical portions andnumerical portions of the vocabularies; combine result sets for themultiple sub-queries; and generate a data visualization of the combinedresult sets; a display device configured to display the datavisualization; and a user interface configured to provide the user witha capability to specify other values for the categorical portions andthe numerical portions of the vocabularies, wherein at least one of themultiple sub-queries is dynamically generated for at least one of theother values specified by the user.